class_docstr: A geometric random variable.
docstr: Evaluate the probability density function at x.
  
  Parameters
  ----------
  x : array-like, shape=[n_samples, *support_shape]
      Sample points at which to compute the probability density function.
  point : array-like, shape=[..., *space.shape]
      Point representing a distribution.
  
  Returns
  -------
  pdf_at_x : array-like, shape=[..., n_samples, *support_shape]
      Values of pdf at x for each value of the parameters provided
      by point.
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code: def pdf(self, x, point):
      """Evaluate the probability density function at x.
  
      Parameters
      ----------
      x : array-like, shape=[n_samples, *support_shape]
          Sample points at which to compute the probability density function.
      point : array-like, shape=[..., *space.shape]
          Point representing a distribution.
  
      Returns
      -------
      pdf_at_x : array-like, shape=[..., n_samples, *support_shape]
          Values of pdf at x for each value of the parameters provided
          by point.
      """
      return gs.from_numpy(
          geom.pmf(x, point),
      )
  
imports: from scipy.stats import geom
  import geomstats.backend
  from geomstats.geometry.base import VectorSpaceOpenSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.information_geometry.base import InformationManifoldMixin, ScipyUnivariateRandomVariable
label_desc: Use image node_id 3 for calling the GeometricDistributionsRandomVariable obj's underlying member method code with example usage: obj.pdf(x, point) and returns: gs